The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 75–80, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-75-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 75–80, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-75-2021

  28 Jun 2021

28 Jun 2021

GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS

M. Lu1, L. Groeneveld1, D. Karssenberg1, S. Ji3, R. Jentink2, E. Paree2, and E. Addink1 M. Lu et al.
  • 1Department of Physical Geography, Utrecht University, the Netherlands
  • 2Rijkswaterstaat CIV, the Netherlands
  • 3Department of remote sensing and information engineering, Wuhan University, China

Keywords: Geomorphology, Inter-tidal flats, semantic segmentation, multi-dimensional, feature interpretation, OBIA, XGBoost

Abstract. Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.